A laser range finder (LRF), which is a kind of a range sensor, is a noninvasive sensor that can be used for anonymous tracking for a pedestrian with high precision in social environment.
For a pedestrian tracking, a laser range finder has many advantages over a sensor of other types.
First, noninvasiveness thereof is a large advantage.
Furthermore, a use of hardware such as a floor pressure sensor may be destructive to public and business district, and a requirement to make people carry with a tag or a portable device will often demand to actively intervene in a social system that is a target for research.
On the other hand, although a video is sometimes used as a tracking tool, the LRF provides far high measurement precision and data processing required is far fewer.
Furthermore, the LRF outputs only anonymous distance information and a concern over privacy is less than a video camera.
Although these advantages must be weighed against a cost of the sensor, even if taking such a point into consideration, the LRF is a popular tool for analyzing action of a human in the public space of high traffic.
It is important that a human tracking system provides exact data consistently, and appropriate calibration of a sensor position is indispensable to it.
An example of a conventional calibration apparatus of a range sensor is disclosed in the patent literature 1. In a movable body position estimation system disclosed in this patent literature 1, a distance to a movable body is measured by each of three or more range sensors that are dispersed mutually in a measurement area, and a position estimation calculation apparatus acquires a sensor measurement value at an arbitrary time from each of the range sensors, and stores them. Distance reliability that indicates a degree of reliability according to the distance is applied to the sensor measurement value that is acquired and stored of each of the range sensors, and positions of the range sensors and the movable body are estimated with using the measurement values having high reliability out of the sensor measurement values that is acquired and stored. Furthermore, position estimation processing performs calibration of the position of each of the range sensors and estimation of a moving position of the movable body with using the sensor measurement values obtained from the two or more range sensors at positions before and after movement of the movable body.
However, since an initial value of each sensor position is required when calculating each sensor position in the movable body position estimation system of the patent literature 1, whenever environment to measure changes, for example, it is necessary to measure the initial value, and it is troublesome.
In order to cope with such a problem, a patent literature 2 discloses a calibration apparatus that can perform calibration of positions and directions of a plurality of range sensors installed in certain environment even if not giving an initial value, for example.
In a technology disclosed in this patent literature 2, a measuring apparatus includes a computer, and a plurality of range sensors are connected to the computer. Based on an output from each sensor, the computer detects a human observation point for each sensor, and calculates a human moving locus from a time change of the human observation point. Next, the moving loci calculated based on outputs of respective sensors are made to be agreed with each other between the sensors. Two human observation points on the agreed moving locus are extracted according to a predetermined rule, and a distance between the sensors that produce the agreed moving locus with using them and restriction between the sensors about relative angles are calculated for each set of the sensors that the moving loci are agreed with each other. Then, using the restrictions between the sensors, the positions and the directions of all the sensors are estimated and the estimated positions are adjusted.